Overview

Dataset statistics

Number of variables24
Number of observations3172
Missing cells3858
Missing cells (%)5.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory726.9 KiB
Average record size in memory234.7 B

Variable types

Numeric19
Categorical4
Boolean1

Alerts

DataYear has constant value "2016"Constant
DefaultData has constant value "False"Constant
BuildingType is highly overall correlated with PrimaryPropertyTypeHigh correlation
ENERGYSTARScore is highly overall correlated with SourceEUI(kBtu/sf)High correlation
Electricity(kWh) is highly overall correlated with PropertyGFABuilding(s) and 4 other fieldsHigh correlation
GHGEmissionsIntensity is highly overall correlated with NaturalGas(therms) and 3 other fieldsHigh correlation
Latitude is highly overall correlated with NeighborhoodHigh correlation
Longitude is highly overall correlated with NeighborhoodHigh correlation
NaturalGas(therms) is highly overall correlated with GHGEmissionsIntensity and 3 other fieldsHigh correlation
Neighborhood is highly overall correlated with Latitude and 1 other fieldsHigh correlation
PrimaryPropertyType is highly overall correlated with BuildingTypeHigh correlation
PropertyGFABuilding(s) is highly overall correlated with Electricity(kWh) and 3 other fieldsHigh correlation
PropertyGFATotal is highly overall correlated with Electricity(kWh) and 3 other fieldsHigh correlation
SiteEUI(kBtu/sf) is highly overall correlated with GHGEmissionsIntensity and 4 other fieldsHigh correlation
SiteEnergyUse(kBtu) is highly overall correlated with Electricity(kWh) and 7 other fieldsHigh correlation
SourceEUI(kBtu/sf) is highly overall correlated with ENERGYSTARScore and 3 other fieldsHigh correlation
TotalGHGEmissions is highly overall correlated with Electricity(kWh) and 6 other fieldsHigh correlation
YearsENERGYSTARCertified has 3061 (96.5%) missing valuesMissing
ENERGYSTARScore has 797 (25.1%) missing valuesMissing
OSEBuildingID has unique valuesUnique
SiteEnergyUse(kBtu) has unique valuesUnique
NumberofBuildings has 90 (2.8%) zerosZeros
PropertyGFAParking has 2694 (84.9%) zerosZeros
SteamUse(kBtu) has 3056 (96.3%) zerosZeros
NaturalGas(therms) has 1189 (37.5%) zerosZeros

Reproduction

Analysis started2025-12-14 16:01:04.856730
Analysis finished2025-12-14 16:01:56.385186
Duration51.53 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

OSEBuildingID
Real number (ℝ)

Unique 

Distinct3172
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21573.597
Minimum1
Maximum50226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.6 KiB
2025-12-14T17:01:56.464244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile334.55
Q120163.75
median23212.5
Q326025.25
95-th percentile49786.45
Maximum50226
Range50225
Interquartile range (IQR)5861.5

Descriptive statistics

Standard deviation11999.826
Coefficient of variation (CV)0.55622741
Kurtosis0.82524736
Mean21573.597
Median Absolute Deviation (MAD)2947
Skewness-0.0090784473
Sum68431450
Variance1.4399583 × 108
MonotonicityNot monotonic
2025-12-14T17:01:56.603505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
21
 
< 0.1%
51
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
101
 
< 0.1%
111
 
< 0.1%
121
 
< 0.1%
131
 
< 0.1%
151
 
< 0.1%
Other values (3162)3162
99.7%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
51
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
111
< 0.1%
121
< 0.1%
131
< 0.1%
151
< 0.1%
ValueCountFrequency (%)
502261
< 0.1%
502251
< 0.1%
502241
< 0.1%
502231
< 0.1%
502211
< 0.1%
502191
< 0.1%
502121
< 0.1%
502081
< 0.1%
502071
< 0.1%
502041
< 0.1%

DataYear
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size178.6 KiB
2016
3172 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters12688
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
20163172
100.0%

Length

2025-12-14T17:01:56.732324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-14T17:01:56.798285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
20163172
100.0%

Most occurring characters

ValueCountFrequency (%)
23172
25.0%
03172
25.0%
13172
25.0%
63172
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)12688
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
23172
25.0%
03172
25.0%
13172
25.0%
63172
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12688
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
23172
25.0%
03172
25.0%
13172
25.0%
63172
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12688
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
23172
25.0%
03172
25.0%
13172
25.0%
63172
25.0%

BuildingType
Categorical

High correlation 

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size178.6 KiB
NonResidential
1403 
Multifamily LR (1-4)
981 
Multifamily MR (5-9)
570 
Multifamily HR (10+)
 
108
Nonresidential COS
 
80
Other values (3)
 
30

Length

Max length20
Median length20
Mean length17.20145
Min length6

Characters and Unicode

Total characters54563
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNonResidential
2nd rowNonResidential
3rd rowNonResidential
4th rowNonResidential
5th rowNonresidential COS

Common Values

ValueCountFrequency (%)
NonResidential1403
44.2%
Multifamily LR (1-4)981
30.9%
Multifamily MR (5-9)570
18.0%
Multifamily HR (10+)108
 
3.4%
Nonresidential COS80
 
2.5%
Campus19
 
0.6%
SPS-District K-1210
 
0.3%
Nonresidential WA1
 
< 0.1%

Length

2025-12-14T17:01:56.885327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-14T17:01:56.983318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
multifamily1659
25.2%
nonresidential1484
22.5%
lr981
14.9%
1-4981
14.9%
mr570
 
8.7%
5-9570
 
8.7%
hr108
 
1.6%
10108
 
1.6%
cos80
 
1.2%
campus19
 
0.3%
Other values (3)21
 
0.3%

Most occurring characters

ValueCountFrequency (%)
i6306
 
11.6%
l4802
 
8.8%
3409
 
6.2%
t3163
 
5.8%
a3162
 
5.8%
R3062
 
5.6%
e2968
 
5.4%
n2968
 
5.4%
M2229
 
4.1%
u1678
 
3.1%
Other values (30)20816
38.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)54563
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i6306
 
11.6%
l4802
 
8.8%
3409
 
6.2%
t3163
 
5.8%
a3162
 
5.8%
R3062
 
5.6%
e2968
 
5.4%
n2968
 
5.4%
M2229
 
4.1%
u1678
 
3.1%
Other values (30)20816
38.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)54563
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i6306
 
11.6%
l4802
 
8.8%
3409
 
6.2%
t3163
 
5.8%
a3162
 
5.8%
R3062
 
5.6%
e2968
 
5.4%
n2968
 
5.4%
M2229
 
4.1%
u1678
 
3.1%
Other values (30)20816
38.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)54563
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i6306
 
11.6%
l4802
 
8.8%
3409
 
6.2%
t3163
 
5.8%
a3162
 
5.8%
R3062
 
5.6%
e2968
 
5.4%
n2968
 
5.4%
M2229
 
4.1%
u1678
 
3.1%
Other values (30)20816
38.2%

PrimaryPropertyType
Categorical

High correlation 

Distinct23
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size178.6 KiB
Low-Rise Multifamily
952 
Mid-Rise Multifamily
553 
Small- and Mid-Sized Office
286 
Other
244 
Warehouse
184 
Other values (18)
953 

Length

Max length27
Median length22
Mean length17.430013
Min length5

Characters and Unicode

Total characters55288
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHotel
2nd rowHotel
3rd rowHotel
4th rowHotel
5th rowOther

Common Values

ValueCountFrequency (%)
Low-Rise Multifamily952
30.0%
Mid-Rise Multifamily553
17.4%
Small- and Mid-Sized Office286
 
9.0%
Other244
 
7.7%
Warehouse184
 
5.8%
Large Office156
 
4.9%
Mixed Use Property128
 
4.0%
High-Rise Multifamily103
 
3.2%
Retail Store85
 
2.7%
Hotel74
 
2.3%
Other values (13)407
12.8%

Length

2025-12-14T17:01:57.122661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
multifamily1608
24.3%
low-rise952
14.4%
mid-rise553
 
8.4%
office480
 
7.3%
and286
 
4.3%
small286
 
4.3%
mid-sized286
 
4.3%
other244
 
3.7%
warehouse196
 
3.0%
large156
 
2.4%
Other values (28)1569
23.7%

Most occurring characters

ValueCountFrequency (%)
i7379
 
13.3%
e4382
 
7.9%
l4211
 
7.6%
3444
 
6.2%
a2948
 
5.3%
t2706
 
4.9%
M2613
 
4.7%
f2608
 
4.7%
-2258
 
4.1%
s2117
 
3.8%
Other values (33)20622
37.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)55288
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i7379
 
13.3%
e4382
 
7.9%
l4211
 
7.6%
3444
 
6.2%
a2948
 
5.3%
t2706
 
4.9%
M2613
 
4.7%
f2608
 
4.7%
-2258
 
4.1%
s2117
 
3.8%
Other values (33)20622
37.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)55288
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i7379
 
13.3%
e4382
 
7.9%
l4211
 
7.6%
3444
 
6.2%
a2948
 
5.3%
t2706
 
4.9%
M2613
 
4.7%
f2608
 
4.7%
-2258
 
4.1%
s2117
 
3.8%
Other values (33)20622
37.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)55288
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i7379
 
13.3%
e4382
 
7.9%
l4211
 
7.6%
3444
 
6.2%
a2948
 
5.3%
t2706
 
4.9%
M2613
 
4.7%
f2608
 
4.7%
-2258
 
4.1%
s2117
 
3.8%
Other values (33)20622
37.3%

Neighborhood
Categorical

High correlation 

Distinct19
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size178.6 KiB
DOWNTOWN
541 
EAST
433 
MAGNOLIA / QUEEN ANNE
412 
GREATER DUWAMISH
351 
NORTHEAST
263 
Other values (14)
1172 

Length

Max length22
Median length16
Mean length10.159206
Min length4

Characters and Unicode

Total characters32225
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDOWNTOWN
2nd rowDOWNTOWN
3rd rowDOWNTOWN
4th rowDOWNTOWN
5th rowDOWNTOWN

Common Values

ValueCountFrequency (%)
DOWNTOWN541
17.1%
EAST433
13.7%
MAGNOLIA / QUEEN ANNE412
13.0%
GREATER DUWAMISH351
11.1%
NORTHEAST263
8.3%
LAKE UNION240
7.6%
NORTHWEST198
 
6.2%
SOUTHWEST147
 
4.6%
NORTH136
 
4.3%
BALLARD120
 
3.8%
Other values (9)331
10.4%

Length

2025-12-14T17:01:57.241298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
downtown541
10.8%
east433
 
8.7%
magnolia412
 
8.2%
412
 
8.2%
queen412
 
8.2%
anne412
 
8.2%
greater351
 
7.0%
duwamish351
 
7.0%
northeast263
 
5.3%
lake240
 
4.8%
Other values (9)1173
23.5%

Most occurring characters

ValueCountFrequency (%)
N3951
12.3%
E3539
11.0%
A3285
10.2%
T2926
 
9.1%
O2559
 
7.9%
1828
 
5.7%
W1778
 
5.5%
S1696
 
5.3%
R1587
 
4.9%
U1228
 
3.8%
Other values (24)7848
24.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)32225
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N3951
12.3%
E3539
11.0%
A3285
10.2%
T2926
 
9.1%
O2559
 
7.9%
1828
 
5.7%
W1778
 
5.5%
S1696
 
5.3%
R1587
 
4.9%
U1228
 
3.8%
Other values (24)7848
24.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)32225
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N3951
12.3%
E3539
11.0%
A3285
10.2%
T2926
 
9.1%
O2559
 
7.9%
1828
 
5.7%
W1778
 
5.5%
S1696
 
5.3%
R1587
 
4.9%
U1228
 
3.8%
Other values (24)7848
24.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)32225
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N3951
12.3%
E3539
11.0%
A3285
10.2%
T2926
 
9.1%
O2559
 
7.9%
1828
 
5.7%
W1778
 
5.5%
S1696
 
5.3%
R1587
 
4.9%
U1228
 
3.8%
Other values (24)7848
24.4%

Latitude
Real number (ℝ)

High correlation 

Distinct2719
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.624785
Minimum47.50224
Maximum47.73387
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.6 KiB
2025-12-14T17:01:57.363850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum47.50224
5-th percentile47.54393
Q147.600962
median47.61918
Q347.657157
95-th percentile47.713065
Maximum47.73387
Range0.23163
Interquartile range (IQR)0.056195

Descriptive statistics

Standard deviation0.047117487
Coefficient of variation (CV)0.00098934803
Kurtosis-0.10822197
Mean47.624785
Median Absolute Deviation (MAD)0.02647
Skewness0.1601006
Sum151065.82
Variance0.0022200576
MonotonicityNot monotonic
2025-12-14T17:01:57.508198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.662469
 
0.3%
47.615987
 
0.2%
47.622086
 
0.2%
47.525495
 
0.2%
47.615435
 
0.2%
47.623955
 
0.2%
47.600714
 
0.1%
47.522544
 
0.1%
47.62394
 
0.1%
47.599384
 
0.1%
Other values (2709)3119
98.3%
ValueCountFrequency (%)
47.502241
< 0.1%
47.509591
< 0.1%
47.510181
< 0.1%
47.510421
< 0.1%
47.510981
< 0.1%
47.511041
< 0.1%
47.511272
0.1%
47.511681
< 0.1%
47.511691
< 0.1%
47.513041
< 0.1%
ValueCountFrequency (%)
47.733871
< 0.1%
47.733751
< 0.1%
47.733681
< 0.1%
47.73361
< 0.1%
47.733571
< 0.1%
47.733511
< 0.1%
47.733311
< 0.1%
47.733161
< 0.1%
47.733151
< 0.1%
47.732791
< 0.1%

Longitude
Real number (ℝ)

High correlation 

Distinct2511
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.33521
Minimum-122.41425
Maximum-122.26028
Zeros0
Zeros (%)0.0%
Negative3172
Negative (%)100.0%
Memory size178.6 KiB
2025-12-14T17:01:57.665922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-122.41425
5-th percentile-122.38643
Q1-122.35076
median-122.33264
Q3-122.32022
95-th percentile-122.29181
Maximum-122.26028
Range0.15397
Interquartile range (IQR)0.0305325

Descriptive statistics

Standard deviation0.026645138
Coefficient of variation (CV)-0.00021780433
Kurtosis0.24891371
Mean-122.33521
Median Absolute Deviation (MAD)0.014895
Skewness-0.17813393
Sum-388047.27
Variance0.00070996337
MonotonicityNot monotonic
2025-12-14T17:01:57.825726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.298988
 
0.3%
-122.353987
 
0.2%
-122.333696
 
0.2%
-122.324686
 
0.2%
-122.317695
 
0.2%
-122.333795
 
0.2%
-122.324175
 
0.2%
-122.330645
 
0.2%
-122.325925
 
0.2%
-122.328114
 
0.1%
Other values (2501)3116
98.2%
ValueCountFrequency (%)
-122.414251
< 0.1%
-122.411821
< 0.1%
-122.411781
< 0.1%
-122.411691
< 0.1%
-122.410371
< 0.1%
-122.410361
< 0.1%
-122.410311
< 0.1%
-122.409761
< 0.1%
-122.409741
< 0.1%
-122.409011
< 0.1%
ValueCountFrequency (%)
-122.260281
< 0.1%
-122.260341
< 0.1%
-122.261662
0.1%
-122.261721
< 0.1%
-122.261771
< 0.1%
-122.26181
< 0.1%
-122.262161
< 0.1%
-122.262231
< 0.1%
-122.262351
< 0.1%
-122.262771
< 0.1%

YearBuilt
Real number (ℝ)

Distinct113
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1968.6337
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.6 KiB
2025-12-14T17:01:57.969879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1908
Q11948
median1975
Q31997
95-th percentile2012
Maximum2015
Range115
Interquartile range (IQR)49

Descriptive statistics

Standard deviation33.219065
Coefficient of variation (CV)0.016874173
Kurtosis-0.88331443
Mean1968.6337
Median Absolute Deviation (MAD)24
Skewness-0.53754265
Sum6244506
Variance1103.5063
MonotonicityNot monotonic
2025-12-14T17:01:58.111952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201467
 
2.1%
200066
 
2.1%
196862
 
2.0%
200860
 
1.9%
198859
 
1.9%
198958
 
1.8%
199957
 
1.8%
197055
 
1.7%
200154
 
1.7%
200254
 
1.7%
Other values (103)2580
81.3%
ValueCountFrequency (%)
190051
1.6%
19017
 
0.2%
190211
 
0.3%
19033
 
0.1%
190414
 
0.4%
19059
 
0.3%
190618
 
0.6%
190731
1.0%
190826
0.8%
190929
0.9%
ValueCountFrequency (%)
201535
1.1%
201467
2.1%
201350
1.6%
201235
1.1%
201115
 
0.5%
201023
 
0.7%
200939
1.2%
200860
1.9%
200741
1.3%
200644
1.4%

NumberofBuildings
Real number (ℝ)

Zeros 

Distinct15
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0655738
Minimum0
Maximum27
Zeros90
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size178.6 KiB
2025-12-14T17:01:58.218374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum27
Range27
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.89681462
Coefficient of variation (CV)0.84162603
Kurtosis387.8697
Mean1.0655738
Median Absolute Deviation (MAD)0
Skewness16.922782
Sum3380
Variance0.80427646
MonotonicityNot monotonic
2025-12-14T17:01:58.312079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
12990
94.3%
090
 
2.8%
236
 
1.1%
321
 
0.7%
412
 
0.4%
59
 
0.3%
63
 
0.1%
83
 
0.1%
102
 
0.1%
111
 
< 0.1%
Other values (5)5
 
0.2%
ValueCountFrequency (%)
090
 
2.8%
12990
94.3%
236
 
1.1%
321
 
0.7%
412
 
0.4%
59
 
0.3%
63
 
0.1%
83
 
0.1%
91
 
< 0.1%
102
 
0.1%
ValueCountFrequency (%)
271
 
< 0.1%
231
 
< 0.1%
161
 
< 0.1%
141
 
< 0.1%
111
 
< 0.1%
102
 
0.1%
91
 
< 0.1%
83
 
0.1%
63
 
0.1%
59
0.3%

NumberofFloors
Real number (ℝ)

Distinct43
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6270492
Minimum0
Maximum99
Zeros14
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size178.6 KiB
2025-12-14T17:01:58.424167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile11.45
Maximum99
Range99
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.8664213
Coefficient of variation (CV)1.0517332
Kurtosis62.009403
Mean4.6270492
Median Absolute Deviation (MAD)1
Skewness5.7213781
Sum14677
Variance23.682056
MonotonicityNot monotonic
2025-12-14T17:01:58.557449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
4666
21.0%
3648
20.4%
1424
13.4%
2394
12.4%
6294
9.3%
5289
9.1%
7144
 
4.5%
859
 
1.9%
1132
 
1.0%
1031
 
1.0%
Other values (33)191
 
6.0%
ValueCountFrequency (%)
014
 
0.4%
1424
13.4%
2394
12.4%
3648
20.4%
4666
21.0%
5289
9.1%
6294
9.3%
7144
 
4.5%
859
 
1.9%
918
 
0.6%
ValueCountFrequency (%)
991
 
< 0.1%
491
 
< 0.1%
423
0.1%
412
0.1%
401
 
< 0.1%
391
 
< 0.1%
381
 
< 0.1%
372
0.1%
362
0.1%
341
 
< 0.1%

PropertyGFATotal
Real number (ℝ)

High correlation 

Distinct3003
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84434.774
Minimum11285
Maximum1256335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.6 KiB
2025-12-14T17:01:58.689981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11285
5-th percentile21275.25
Q128168
median43480.5
Q388637.5
95-th percentile302852.5
Maximum1256335
Range1245050
Interquartile range (IQR)60469.5

Descriptive statistics

Standard deviation113992.01
Coefficient of variation (CV)1.35006
Kurtosis26.580056
Mean84434.774
Median Absolute Deviation (MAD)19211
Skewness4.2901395
Sum2.678271 × 108
Variance1.2994178 × 1010
MonotonicityNot monotonic
2025-12-14T17:01:58.843237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
360009
 
0.3%
259208
 
0.3%
288007
 
0.2%
216007
 
0.2%
240006
 
0.2%
302404
 
0.1%
223204
 
0.1%
307204
 
0.1%
319003
 
0.1%
231003
 
0.1%
Other values (2993)3117
98.3%
ValueCountFrequency (%)
112851
< 0.1%
116851
< 0.1%
119681
< 0.1%
127691
< 0.1%
131571
< 0.1%
141011
< 0.1%
160001
< 0.1%
163001
< 0.1%
167951
< 0.1%
182581
< 0.1%
ValueCountFrequency (%)
12563351
< 0.1%
12490551
< 0.1%
12066701
< 0.1%
11721271
< 0.1%
11234351
< 0.1%
10745521
< 0.1%
10524691
< 0.1%
9342921
< 0.1%
9205981
< 0.1%
8617021
< 0.1%

PropertyGFAParking
Real number (ℝ)

Zeros 

Distinct470
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7495.4092
Minimum0
Maximum407795
Zeros2694
Zeros (%)84.9%
Negative0
Negative (%)0.0%
Memory size178.6 KiB
2025-12-14T17:01:58.982778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile44745.1
Maximum407795
Range407795
Interquartile range (IQR)0

Descriptive statistics

Standard deviation28997.958
Coefficient of variation (CV)3.8687625
Kurtosis47.473691
Mean7495.4092
Median Absolute Deviation (MAD)0
Skewness6.0498259
Sum23775438
Variance8.4088156 × 108
MonotonicityNot monotonic
2025-12-14T17:01:59.130122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02694
84.9%
133203
 
0.1%
220002
 
0.1%
300002
 
0.1%
108002
 
0.1%
258002
 
0.1%
129602
 
0.1%
1001762
 
0.1%
204162
 
0.1%
76001
 
< 0.1%
Other values (460)460
 
14.5%
ValueCountFrequency (%)
02694
84.9%
381
 
< 0.1%
2601
 
< 0.1%
4151
 
< 0.1%
6041
 
< 0.1%
7561
 
< 0.1%
8001
 
< 0.1%
9191
 
< 0.1%
12631
 
< 0.1%
13921
 
< 0.1%
ValueCountFrequency (%)
4077951
< 0.1%
3689801
< 0.1%
3351091
< 0.1%
3037071
< 0.1%
2856881
< 0.1%
2729001
< 0.1%
2392521
< 0.1%
2286681
< 0.1%
2065971
< 0.1%
2065801
< 0.1%

PropertyGFABuilding(s)
Real number (ℝ)

High correlation 

Distinct3000
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76939.364
Minimum3636
Maximum1172127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.6 KiB
2025-12-14T17:01:59.275648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3636
5-th percentile21012.15
Q127485.25
median42295.5
Q382015.25
95-th percentile261089.5
Maximum1172127
Range1168491
Interquartile range (IQR)54530

Descriptive statistics

Standard deviation99287.823
Coefficient of variation (CV)1.2904685
Kurtosis26.47592
Mean76939.364
Median Absolute Deviation (MAD)18149
Skewness4.3020431
Sum2.4405166 × 108
Variance9.8580717 × 109
MonotonicityNot monotonic
2025-12-14T17:01:59.415623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
360009
 
0.3%
259208
 
0.3%
288007
 
0.2%
216007
 
0.2%
240006
 
0.2%
307204
 
0.1%
302404
 
0.1%
223204
 
0.1%
450003
 
0.1%
252003
 
0.1%
Other values (2990)3117
98.3%
ValueCountFrequency (%)
36361
< 0.1%
109251
< 0.1%
112851
< 0.1%
114401
< 0.1%
116851
< 0.1%
119681
< 0.1%
127691
< 0.1%
128061
< 0.1%
131571
< 0.1%
141011
< 0.1%
ValueCountFrequency (%)
11721271
< 0.1%
10479341
< 0.1%
10048131
< 0.1%
9706471
< 0.1%
9624281
< 0.1%
9342921
< 0.1%
8880491
< 0.1%
8617021
< 0.1%
7945921
< 0.1%
7913961
< 0.1%

YearsENERGYSTARCertified
Real number (ℝ)

Missing 

Distinct57
Distinct (%)51.4%
Missing3061
Missing (%)96.5%
Infinite0
Infinite (%)0.0%
Mean1.8163978 × 1057
Minimum2007
Maximum2.0162015 × 1059
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.6 KiB
2025-12-14T17:01:59.554476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2007
5-th percentile2010
Q12016
median20162015
Q32.0147013 × 1015
95-th percentile1.0082016 × 1031
Maximum2.0162015 × 1059
Range2.0162015 × 1059
Interquartile range (IQR)2.0147013 × 1015

Descriptive statistics

Standard deviation1.9136938 × 1058
Coefficient of variation (CV)10.535654
Kurtosis111
Mean1.8163978 × 1057
Median Absolute Deviation (MAD)20160002
Skewness10.535654
Sum2.0162015 × 1059
Variance3.6622239 × 10116
MonotonicityNot monotonic
2025-12-14T17:01:59.716512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201613
 
0.4%
201720168
 
0.3%
20177
 
0.2%
20146
 
0.2%
201620156
 
0.2%
20094
 
0.1%
20134
 
0.1%
201720153
 
0.1%
201520143
 
0.1%
2.01620152 × 10113
 
0.1%
Other values (47)54
 
1.7%
(Missing)3061
96.5%
ValueCountFrequency (%)
20071
 
< 0.1%
20094
 
0.1%
20102
 
0.1%
20111
 
< 0.1%
20121
 
< 0.1%
20134
 
0.1%
20146
0.2%
20152
 
0.1%
201613
0.4%
20177
0.2%
ValueCountFrequency (%)
2.01620152 × 10591
< 0.1%
2.01620152 × 10511
< 0.1%
2.01720162 × 10391
< 0.1%
2.01620152 × 10351
< 0.1%
2.01620152 × 10311
< 0.1%
2.01620152 × 10311
< 0.1%
2.01720162 × 10271
< 0.1%
2.01720152 × 10271
< 0.1%
2.01620152 × 10272
0.1%
2.01620132 × 10271
< 0.1%

ENERGYSTARScore
Real number (ℝ)

High correlation  Missing 

Distinct100
Distinct (%)4.2%
Missing797
Missing (%)25.1%
Infinite0
Infinite (%)0.0%
Mean67.231579
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.6 KiB
2025-12-14T17:01:59.864344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q152
median74
Q389
95-th percentile99
Maximum100
Range99
Interquartile range (IQR)37

Descriptive statistics

Standard deviation26.941015
Coefficient of variation (CV)0.40071965
Kurtosis-0.29888023
Mean67.231579
Median Absolute Deviation (MAD)18
Skewness-0.81724458
Sum159675
Variance725.81829
MonotonicityNot monotonic
2025-12-14T17:02:00.007568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10087
 
2.7%
9870
 
2.2%
9662
 
2.0%
8952
 
1.6%
9351
 
1.6%
9549
 
1.5%
9147
 
1.5%
9947
 
1.5%
9246
 
1.5%
8146
 
1.5%
Other values (90)1818
57.3%
(Missing)797
25.1%
ValueCountFrequency (%)
133
1.0%
210
 
0.3%
313
 
0.4%
45
 
0.2%
58
 
0.3%
68
 
0.3%
710
 
0.3%
89
 
0.3%
95
 
0.2%
109
 
0.3%
ValueCountFrequency (%)
10087
2.7%
9947
1.5%
9870
2.2%
9743
1.4%
9662
2.0%
9549
1.5%
9444
1.4%
9351
1.6%
9246
1.5%
9147
1.5%

SiteEUI(kBtu/sf)
Real number (ℝ)

High correlation 

Distinct1044
Distinct (%)32.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.419672
Minimum1.4
Maximum834.40002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.6 KiB
2025-12-14T17:02:00.146700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.4
5-th percentile18.700001
Q128.1
median38.950001
Q360.900002
95-th percentile139.745
Maximum834.40002
Range833.00002
Interquartile range (IQR)32.800001

Descriptive statistics

Standard deviation53.207069
Coefficient of variation (CV)0.97771757
Kurtosis40.694322
Mean54.419672
Median Absolute Deviation (MAD)13.650002
Skewness4.9191634
Sum172619.2
Variance2830.9922
MonotonicityNot monotonic
2025-12-14T17:02:00.286338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.7000007617
 
0.5%
24.2000007616
 
0.5%
28.7999992415
 
0.5%
3215
 
0.5%
26.3999996214
 
0.4%
28.8999996213
 
0.4%
22.7999992413
 
0.4%
30.6000003813
 
0.4%
29.6000003813
 
0.4%
2912
 
0.4%
Other values (1034)3031
95.6%
ValueCountFrequency (%)
1.3999999761
< 0.1%
2.0999999051
< 0.1%
2.2999999521
< 0.1%
31
< 0.1%
3.2000000481
< 0.1%
3.52
0.1%
3.5999999052
0.1%
3.7999999521
< 0.1%
4.3000001911
< 0.1%
4.4000000951
< 0.1%
ValueCountFrequency (%)
834.40002441
< 0.1%
696.70001221
< 0.1%
694.70001221
< 0.1%
593.59997561
< 0.1%
465.51
< 0.1%
456.60000611
< 0.1%
438.20001221
< 0.1%
412.70001221
< 0.1%
404.10000611
< 0.1%
400.79998781
< 0.1%

SourceEUI(kBtu/sf)
Real number (ℝ)

High correlation 

Distinct1589
Distinct (%)50.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.05807
Minimum0
Maximum2620
Zeros6
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size178.6 KiB
2025-12-14T17:02:00.421681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43.054999
Q175.300003
median97.049999
Q3144.89999
95-th percentile333.045
Maximum2620
Range2620
Interquartile range (IQR)69.599991

Descriptive statistics

Standard deviation126.23938
Coefficient of variation (CV)0.94875404
Kurtosis80.907568
Mean133.05807
Median Absolute Deviation (MAD)27.850002
Skewness6.3102251
Sum422060.2
Variance15936.382
MonotonicityNot monotonic
2025-12-14T17:02:00.559194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87.699996958
 
0.3%
83.699996958
 
0.3%
73.099998478
 
0.3%
68.099998478
 
0.3%
69.699996958
 
0.3%
958
 
0.3%
78.599998478
 
0.3%
847
 
0.2%
68.900001537
 
0.2%
63.299999247
 
0.2%
Other values (1579)3095
97.6%
ValueCountFrequency (%)
06
0.2%
4.51
 
< 0.1%
6.5999999052
 
0.1%
6.9000000951
 
< 0.1%
91
 
< 0.1%
9.51
 
< 0.1%
9.8999996191
 
< 0.1%
10.199999811
 
< 0.1%
11.100000381
 
< 0.1%
11.199999811
 
< 0.1%
ValueCountFrequency (%)
26201
< 0.1%
2181.3000491
< 0.1%
1527.3000491
< 0.1%
1206.6999511
< 0.1%
912.79998781
< 0.1%
855.20001221
< 0.1%
851.09997561
< 0.1%
835.79998781
< 0.1%
781.29998781
< 0.1%
781.09997561
< 0.1%

SiteEnergyUse(kBtu)
Real number (ℝ)

High correlation  Unique 

Distinct3172
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4189992.5
Minimum57133.199
Maximum98960776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.6 KiB
2025-12-14T17:02:00.695544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum57133.199
5-th percentile520911.44
Q1934115.56
median1787633.5
Q34167798.2
95-th percentile16279284
Maximum98960776
Range98903643
Interquartile range (IQR)3233682.6

Descriptive statistics

Standard deviation7132304.3
Coefficient of variation (CV)1.7022236
Kurtosis34.42321
Mean4189992.5
Median Absolute Deviation (MAD)1047125.3
Skewness4.8456996
Sum1.3290656 × 1010
Variance5.0869764 × 1013
MonotonicityNot monotonic
2025-12-14T17:02:00.842834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7226362.51
 
< 0.1%
83879331
 
< 0.1%
67945841
 
< 0.1%
141726061
 
< 0.1%
120866161
 
< 0.1%
57587951
 
< 0.1%
6298131.51
 
< 0.1%
137238201
 
< 0.1%
45737771
 
< 0.1%
160166441
 
< 0.1%
Other values (3162)3162
99.7%
ValueCountFrequency (%)
57133.199221
< 0.1%
79711.796881
< 0.1%
90558.703131
< 0.1%
97690.398441
< 0.1%
1069181
< 0.1%
111969.70311
< 0.1%
1131301
< 0.1%
116486.60161
< 0.1%
117438.39841
< 0.1%
123767.20311
< 0.1%
ValueCountFrequency (%)
989607761
< 0.1%
906096401
< 0.1%
680907281
< 0.1%
653369801
< 0.1%
650472841
< 0.1%
591076201
< 0.1%
587613041
< 0.1%
577644081
< 0.1%
564852041
< 0.1%
531661561
< 0.1%

SteamUse(kBtu)
Real number (ℝ)

Zeros 

Distinct117
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean143271.3
Minimum0
Maximum31030194
Zeros3056
Zeros (%)96.3%
Negative0
Negative (%)0.0%
Memory size178.6 KiB
2025-12-14T17:02:00.976317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum31030194
Range31030194
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1265004.2
Coefficient of variation (CV)8.8294321
Kurtosis266.6715
Mean143271.3
Median Absolute Deviation (MAD)0
Skewness14.654731
Sum4.5445657 × 108
Variance1.6002357 × 1012
MonotonicityNot monotonic
2025-12-14T17:02:01.117616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03056
96.3%
20038821
 
< 0.1%
2214446.251
 
< 0.1%
2276286.51
 
< 0.1%
1039735.1881
 
< 0.1%
5237165.51
 
< 0.1%
5999360.51
 
< 0.1%
1656352.51
 
< 0.1%
4337738.51
 
< 0.1%
4870847.51
 
< 0.1%
Other values (107)107
 
3.4%
ValueCountFrequency (%)
03056
96.3%
21230.800781
 
< 0.1%
1379001
 
< 0.1%
151742.51
 
< 0.1%
166488.40631
 
< 0.1%
1757801
 
< 0.1%
180731.79691
 
< 0.1%
2046501
 
< 0.1%
230989.29691
 
< 0.1%
2662621
 
< 0.1%
ValueCountFrequency (%)
310301941
< 0.1%
284388841
< 0.1%
196547621
< 0.1%
185478581
< 0.1%
175484161
< 0.1%
162845701
< 0.1%
155860141
< 0.1%
135562191
< 0.1%
132962491
< 0.1%
127609711
< 0.1%

Electricity(kWh)
Real number (ℝ)

High correlation 

Distinct3168
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean841083.03
Minimum0
Maximum12562766
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size178.6 KiB
2025-12-14T17:02:01.264797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile78250.588
Q1187476.23
median344529.95
Q3824027.81
95-th percentile3432258.2
Maximum12562766
Range12562766
Interquartile range (IQR)636551.59

Descriptive statistics

Standard deviation1424810.2
Coefficient of variation (CV)1.6940184
Kurtosis20.331119
Mean841083.03
Median Absolute Deviation (MAD)199438.55
Skewness4.0599032
Sum2.6679154 × 109
Variance2.030084 × 1012
MonotonicityNot monotonic
2025-12-14T17:02:01.412487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03
 
0.1%
239011.59382
 
0.1%
317841.40632
 
0.1%
89109.898441
 
< 0.1%
631404.31251
 
< 0.1%
192884.09381
 
< 0.1%
803639.68751
 
< 0.1%
857081.3751
 
< 0.1%
403017.51
 
< 0.1%
280844.90631
 
< 0.1%
Other values (3158)3158
99.6%
ValueCountFrequency (%)
03
0.1%
11
 
< 0.1%
1798.9000241
 
< 0.1%
4913.51
 
< 0.1%
7727.2001951
 
< 0.1%
16744.800781
 
< 0.1%
188901
 
< 0.1%
19771.900391
 
< 0.1%
21051.300781
 
< 0.1%
21349.599611
 
< 0.1%
ValueCountFrequency (%)
125627661
< 0.1%
122158951
< 0.1%
120790091
< 0.1%
119694131
< 0.1%
119057391
< 0.1%
115837771
< 0.1%
115369621
< 0.1%
112482091
< 0.1%
111315081
< 0.1%
109897621
< 0.1%

NaturalGas(therms)
Real number (ℝ)

High correlation  Zeros 

Distinct1983
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11516.549
Minimum0
Maximum679905.38
Zeros1189
Zeros (%)37.5%
Negative0
Negative (%)0.0%
Memory size178.6 KiB
2025-12-14T17:02:01.553714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3110.725
Q311604.185
95-th percentile47152.69
Maximum679905.38
Range679905.38
Interquartile range (IQR)11604.185

Descriptive statistics

Standard deviation28956.117
Coefficient of variation (CV)2.5143051
Kurtosis156.38481
Mean11516.549
Median Absolute Deviation (MAD)3110.725
Skewness9.6310007
Sum36530492
Variance8.384567 × 108
MonotonicityNot monotonic
2025-12-14T17:02:01.695102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01189
37.5%
2268.4602052
 
0.1%
22419.226561
 
< 0.1%
8027.5698241
 
< 0.1%
21940.830081
 
< 0.1%
6198.1503911
 
< 0.1%
2864.8999021
 
< 0.1%
21757.400391
 
< 0.1%
3608.3596191
 
< 0.1%
5403.6293951
 
< 0.1%
Other values (1973)1973
62.2%
ValueCountFrequency (%)
01189
37.5%
0.3299999541
 
< 0.1%
1.5300000911
 
< 0.1%
2.1999998091
 
< 0.1%
3.3200001721
 
< 0.1%
3.759999991
 
< 0.1%
7.0809097291
 
< 0.1%
7.6388001441
 
< 0.1%
8.8299999241
 
< 0.1%
9.4699993131
 
< 0.1%
ValueCountFrequency (%)
679905.3751
< 0.1%
560966.1251
< 0.1%
346853.31251
< 0.1%
328535.1251
< 0.1%
3096731
< 0.1%
300613.251
< 0.1%
296021.51
< 0.1%
256831.6251
< 0.1%
224136.60941
< 0.1%
206266.31251
< 0.1%

DefaultData
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.9 KiB
False
3172 
ValueCountFrequency (%)
False3172
100.0%
2025-12-14T17:02:01.782791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

TotalGHGEmissions
Real number (ℝ)

High correlation 

Distinct2665
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.229543
Minimum0
Maximum3768.66
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size178.6 KiB
2025-12-14T17:02:01.873320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.9155
Q19.4975
median33.135
Q391.5
95-th percentile358.813
Maximum3768.66
Range3768.66
Interquartile range (IQR)82.0025

Descriptive statistics

Standard deviation203.33469
Coefficient of variation (CV)2.204659
Kurtosis88.514066
Mean92.229543
Median Absolute Deviation (MAD)27.165
Skewness7.6159241
Sum292552.11
Variance41344.997
MonotonicityNot monotonic
2025-12-14T17:02:02.008132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.957
 
0.2%
4.26
 
0.2%
4.745
 
0.2%
3.635
 
0.2%
9.295
 
0.2%
4.025
 
0.2%
4.155
 
0.2%
5.465
 
0.2%
6.185
 
0.2%
4.85
 
0.2%
Other values (2655)3119
98.3%
ValueCountFrequency (%)
02
0.1%
0.41
< 0.1%
0.631
< 0.1%
0.681
< 0.1%
0.751
< 0.1%
0.791
< 0.1%
0.811
< 0.1%
0.821
< 0.1%
0.861
< 0.1%
0.871
< 0.1%
ValueCountFrequency (%)
3768.661
< 0.1%
3278.111
< 0.1%
2573.751
< 0.1%
2549.471
< 0.1%
2489.781
< 0.1%
2055.821
< 0.1%
1990.51
< 0.1%
1789.691
< 0.1%
1727.111
< 0.1%
1699.451
< 0.1%

GHGEmissionsIntensity
Real number (ℝ)

High correlation 

Distinct497
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.159773
Minimum0
Maximum25.71
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size178.6 KiB
2025-12-14T17:02:02.144911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.13
Q10.21
median0.61
Q31.37
95-th percentile3.8935
Maximum25.71
Range25.71
Interquartile range (IQR)1.16

Descriptive statistics

Standard deviation1.7053766
Coefficient of variation (CV)1.47044
Kurtosis33.360598
Mean1.159773
Median Absolute Deviation (MAD)0.43
Skewness4.5157544
Sum3678.8
Variance2.9083093
MonotonicityNot monotonic
2025-12-14T17:02:02.288108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1798
 
3.1%
0.1596
 
3.0%
0.1690
 
2.8%
0.1882
 
2.6%
0.1973
 
2.3%
0.1366
 
2.1%
0.264
 
2.0%
0.2160
 
1.9%
0.1460
 
1.9%
0.2352
 
1.6%
Other values (487)2431
76.6%
ValueCountFrequency (%)
02
 
0.1%
0.012
 
0.1%
0.024
 
0.1%
0.035
 
0.2%
0.047
0.2%
0.056
0.2%
0.0614
0.4%
0.077
0.2%
0.087
0.2%
0.0910
0.3%
ValueCountFrequency (%)
25.711
< 0.1%
16.991
< 0.1%
16.931
< 0.1%
16.381
< 0.1%
15.421
< 0.1%
14.941
< 0.1%
14.891
< 0.1%
14.321
< 0.1%
13.981
< 0.1%
13.881
< 0.1%

Interactions

2025-12-14T17:01:53.688740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:06.038316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:08.236538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:10.375064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:12.783413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:15.019753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:17.163233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:19.271984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:21.515182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:23.877839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:26.134058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:35.067425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:37.161609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:39.252582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:41.929084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:44.134447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:46.331772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:48.531540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:51.468957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:53.789155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:06.137315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:08.335231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:10.491242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:12.885085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:15.117517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:17.263405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:19.375453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:21.623573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:23.984764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:26.446428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:35.162418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:37.256734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:39.352775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:42.030951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:44.238666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:46.434165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:48.638456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:51.572608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:53.879998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:06.231644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:08.427131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:10.601051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:12.983897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:15.210937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:17.357382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:19.478835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:21.729469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:24.087345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:26.771796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:35.255033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:37.349491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:39.445465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:42.128502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:44.343387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:46.530704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:48.740296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:51.672469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:53.987652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:06.340142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:08.538869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:10.716862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:13.106613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:15.327856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:17.471876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:19.594661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:21.853406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:24.204294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:27.084903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:35.364286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:37.458909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:39.558113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-14T17:01:51.786967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:54.090586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:06.438421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:08.637916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:10.826903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:13.210763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:15.430158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:17.577486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:19.717044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:21.968071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:24.311372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:27.400334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:35.466387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:37.560417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:39.658717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:42.349481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:44.559767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:46.750246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:48.971026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:51.893002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:54.187615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:06.535928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:08.733053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:10.936997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:13.315285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:15.521616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:17.673491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:19.820376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:22.077978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-14T17:01:07.920882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:10.069762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:12.429068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:14.695791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:16.855013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:18.971250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:21.189426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:23.547603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:25.797337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:34.125990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:36.859904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:38.957848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:41.612576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:43.814550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:46.009451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:48.218517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:50.500818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:53.374083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:55.631764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:08.028900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:10.176754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:12.552746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:14.805887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:16.964655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:19.073417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:21.299999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:23.659100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:25.909701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:34.446410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:36.958918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:39.059161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:41.726103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:43.925268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:46.119828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:48.327893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:50.611771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:53.484131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:55.738762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:08.137281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:10.280297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:12.672072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:14.913579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:17.067815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:19.173065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:21.412923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:23.770994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:26.027020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:34.756321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:37.059006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:39.159634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:41.832256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:44.034729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:46.231520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:48.434523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:50.731767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-14T17:01:53.587961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-14T17:02:03.248523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
BuildingTypeENERGYSTARScoreElectricity(kWh)GHGEmissionsIntensityLatitudeLongitudeNaturalGas(therms)NeighborhoodNumberofBuildingsNumberofFloorsOSEBuildingIDPrimaryPropertyTypePropertyGFABuilding(s)PropertyGFAParkingPropertyGFATotalSiteEUI(kBtu/sf)SiteEnergyUse(kBtu)SourceEUI(kBtu/sf)SteamUse(kBtu)TotalGHGEmissionsYearBuiltYearsENERGYSTARCertified
BuildingType1.0000.1140.1520.0970.1440.1200.1130.1970.1880.3110.1970.6750.1290.0510.1250.1300.1390.1080.0520.1200.1560.000
ENERGYSTARScore0.1141.000-0.185-0.2320.092-0.040-0.0590.0610.0520.1570.1000.1180.0810.0170.082-0.454-0.185-0.515-0.022-0.1120.0820.235
Electricity(kWh)0.152-0.1851.0000.158-0.046-0.0050.2080.0760.0350.344-0.2450.2610.7510.3300.7710.4870.8590.6540.1580.5560.3020.404
GHGEmissionsIntensity0.097-0.2320.1581.000-0.1070.0500.8180.0140.003-0.110-0.0890.2380.053-0.0090.0480.7580.5460.4320.2060.823-0.202-0.292
Latitude0.1440.092-0.046-0.1071.000-0.014-0.0690.6120.0580.0600.0920.216-0.0560.019-0.046-0.080-0.088-0.047-0.111-0.1080.150-0.168
Longitude0.120-0.040-0.0050.050-0.0141.0000.0260.5070.026-0.1060.1400.149-0.024-0.050-0.0290.0450.0170.034-0.0120.020-0.0500.170
NaturalGas(therms)0.113-0.0590.2080.818-0.0690.0261.0000.0000.0260.012-0.0540.2870.2900.0570.2860.5280.5660.216-0.0290.835-0.018-0.306
Neighborhood0.1970.0610.0760.0140.6120.5070.0001.0000.0120.1710.1600.1990.0680.0420.0660.1090.0800.0250.0000.0120.1780.000
NumberofBuildings0.1880.0520.0350.0030.0580.0260.0260.0121.000-0.0270.0100.1320.0550.0060.056-0.0110.042-0.018-0.0000.0360.038-0.099
NumberofFloors0.3110.1570.344-0.1100.060-0.1060.0120.171-0.0271.000-0.0110.3490.4530.2500.460-0.0020.2860.0620.1430.1750.3010.427
OSEBuildingID0.1970.100-0.245-0.0890.0920.140-0.0540.1600.010-0.0111.0000.250-0.245-0.202-0.259-0.187-0.260-0.197-0.174-0.2070.171-0.188
PrimaryPropertyType0.6750.1180.2610.2380.2160.1490.2870.1990.1320.3490.2501.0000.1800.1570.1920.2840.2830.2760.1260.2670.1890.000
PropertyGFABuilding(s)0.1290.0810.7510.053-0.056-0.0240.2900.0680.0550.453-0.2450.1801.0000.2270.9830.1490.7410.1750.1580.5600.2870.325
PropertyGFAParking0.0510.0170.330-0.0090.019-0.0500.0570.0420.0060.250-0.2020.1570.2271.0000.3540.1950.3080.2390.0410.2100.2400.406
PropertyGFATotal0.1250.0820.7710.048-0.046-0.0290.2860.0660.0560.460-0.2590.1920.9830.3541.0000.1740.7570.2060.1540.5650.3160.420
SiteEUI(kBtu/sf)0.130-0.4540.4870.758-0.0800.0450.5280.109-0.011-0.002-0.1870.2840.1490.1950.1741.0000.7040.8720.1650.711-0.0640.125
SiteEnergyUse(kBtu)0.139-0.1850.8590.546-0.0880.0170.5660.0800.0420.286-0.2600.2830.7410.3080.7570.7041.0000.6360.2000.8730.1600.382
SourceEUI(kBtu/sf)0.108-0.5150.6540.432-0.0470.0340.2160.025-0.0180.062-0.1970.2760.1750.2390.2060.8720.6361.0000.1420.4670.0560.197
SteamUse(kBtu)0.052-0.0220.1580.206-0.111-0.012-0.0290.000-0.0000.143-0.1740.1260.1580.0410.1540.1650.2000.1421.0000.244-0.1470.256
TotalGHGEmissions0.120-0.1120.5560.823-0.1080.0200.8350.0120.0360.175-0.2070.2670.5600.2100.5650.7110.8730.4670.2441.0000.0230.184
YearBuilt0.1560.0820.302-0.2020.150-0.050-0.0180.1780.0380.3010.1710.1890.2870.2400.316-0.0640.1600.056-0.1470.0231.000-0.014
YearsENERGYSTARCertified0.0000.2350.404-0.292-0.1680.170-0.3060.000-0.0990.427-0.1880.0000.3250.4060.4200.1250.3820.1970.2560.184-0.0141.000

Missing values

2025-12-14T17:01:55.912725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-14T17:01:56.128227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-12-14T17:01:56.316534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

OSEBuildingIDDataYearBuildingTypePrimaryPropertyTypeNeighborhoodLatitudeLongitudeYearBuiltNumberofBuildingsNumberofFloorsPropertyGFATotalPropertyGFAParkingPropertyGFABuilding(s)YearsENERGYSTARCertifiedENERGYSTARScoreSiteEUI(kBtu/sf)SourceEUI(kBtu/sf)SiteEnergyUse(kBtu)SteamUse(kBtu)Electricity(kWh)NaturalGas(therms)DefaultDataTotalGHGEmissionsGHGEmissionsIntensity
012016NonResidentialHotelDOWNTOWN47.61220-122.3379919271.01288434088434NaN60.081.699997182.5000007226362.52003882.0001.156514e+0612764.529300False249.982.83
122016NonResidentialHotelDOWNTOWN47.61317-122.3339319961.0111035661506488502NaN61.094.800003176.1000068387933.00.0009.504252e+0551450.816410False295.862.86
352016NonResidentialHotelDOWNTOWN47.61412-122.3366419261.01061320061320NaN56.0110.800003216.1999976794584.02214446.2508.115253e+0518112.130860False286.434.67
482016NonResidentialHotelDOWNTOWN47.61375-122.3404719801.01817558062000113580NaN75.0114.800003211.39999414172606.00.0001.573449e+0688039.984380False505.012.88
592016Nonresidential COSOtherDOWNTOWN47.61623-122.3365719991.02972883719860090NaNNaN136.100006316.29998812086616.00.0002.160444e+0647151.816410False301.813.10
6102016NonResidentialHotelDOWNTOWN47.61390-122.3328319261.01183008083008NaN27.070.800003146.6000065758795.00.0008.239199e+0529475.800780False176.142.12
7112016NonResidentialOtherDOWNTOWN47.61327-122.3313619261.081027610102761NaNNaN61.299999141.6999976298131.52276286.5001.065843e+063851.890137False221.512.16
8122016NonResidentialHotelDOWNTOWN47.60294-122.3326319041.0151639840163984NaN43.083.699997180.89999413723820.00.0002.138898e+0664259.000000False392.162.39
9132016Multifamily MR (5-9)Mid-Rise MultifamilyDOWNTOWN47.60284-122.3318419101.0663712149662216NaN1.081.500000182.6999974573777.01039735.1887.420912e+0510020.259770False151.122.37
10152016NonResidentialHotelDOWNTOWN47.60695-122.3341419691.01115316319279133884NaN30.0119.599998228.19999716016644.05237165.5001.813490e+0645918.500000False691.264.51
OSEBuildingIDDataYearBuildingTypePrimaryPropertyTypeNeighborhoodLatitudeLongitudeYearBuiltNumberofBuildingsNumberofFloorsPropertyGFATotalPropertyGFAParkingPropertyGFABuilding(s)YearsENERGYSTARCertifiedENERGYSTARScoreSiteEUI(kBtu/sf)SourceEUI(kBtu/sf)SiteEnergyUse(kBtu)SteamUse(kBtu)Electricity(kWh)NaturalGas(therms)DefaultDataTotalGHGEmissionsGHGEmissionsIntensity
3363502042016Nonresidential COSOtherNORTH47.72126-122.2973519491.0111285011285NaNNaN57.200001140.0000006.456654e+050.0126552.00002138.700195False14.371.27
3364502072016Nonresidential COSOtherBALLARD47.67295-122.3922819111.0116795016795NaNNaN55.799999126.0000009.366165e+050.0158890.50003944.819824False24.731.47
3365502082016Nonresidential COSOtherBALLARD47.67734-122.3762419721.0112769012769NaNNaN400.799988618.0999765.117308e+060.0353216.093839121.351560False216.1816.93
3367502122016Nonresidential COSOtherEAST47.63228-122.3157419121.0123445023445NaNNaN254.899994380.1000065.976246e+060.0369539.812547153.757810False259.2211.06
3368502192016Nonresidential COSMixed Use PropertyCENTRAL47.60775-122.3022519941.0120050020050NaNNaN90.400002175.1999971.813404e+060.0225513.796910439.510740False60.813.03
3370502212016Nonresidential COSOtherDELRIDGE NEIGHBORHOODS47.54067-122.3744119821.0118261018261NaNNaN51.000000126.0000009.320821e+050.0185334.70312997.199951False20.331.11
3372502232016Nonresidential COSOtherDOWNTOWN47.59625-122.3228320041.0116000016000NaNNaN59.400002114.1999979.502762e+050.0116221.00005537.299805False32.172.01
3373502242016Nonresidential COSOtherMAGNOLIA / QUEEN ANNE47.63644-122.3578419741.0113157013157NaNNaN438.200012744.7999885.765898e+060.0525251.687539737.390630False223.5416.99
3374502252016Nonresidential COSMixed Use PropertyGREATER DUWAMISH47.52832-122.3243119891.0114101014101NaNNaN51.000000105.3000037.194712e+050.0102248.00003706.010010False22.111.57
3375502262016Nonresidential COSMixed Use PropertyGREATER DUWAMISH47.53939-122.2953619381.0118258018258NaNNaN63.099998115.8000031.152896e+060.0126774.39847203.419922False41.272.26